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This article details the integration of Fortanix Confidential AI with CNCF Confidential Containers, enabling enterprises to verify data protection during inference within Kubernetes environments. By leveraging hardware-based Trusted Execution Environments, this solution addresses the critical gap where traditional encryption fails to protect model weights and sensitive data while they are actively processing in memory. The development represents a significant step forward in building a full-stack trusted AI infrastructure that balances operational utility with strict privacy requirements for high-value sectors like finance and healthcare.

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Cisco and Fortanix help make confidential AI verifiable from data to Kubernetes

Cisco BlogsOctober 21, 20267 min read

Fortanix Confidential AI now supports CNCF Confidential Containers. Discover how Cisco, Fortanix, and VAST Data help enterprises run secure frontier AI, ensuring sensitive models and data remain protected and verifiable while in use.

Fortanix Confidential AI now supports CNCF Confidential Containers. Here is why that matters for enterprises building on Cisco Secure AI Factory with NVIDIA, and how it fits a full-stack approach to trusted AI. The AI use cases with the most value tend to sit on the most sensitive data. A bank wants models working on transaction records. A hospital wants insight from clinical data. A government agency wants AI on information that can never leave its jurisdiction. For these organizations, the question is no longer whether AI works. It is whether they can verify that protections are in place for their data and the models working on it while AI is running. That is harder than it sounds. Encryption protects data at rest and in transit, but during inference, model weights and data are decrypted in CPU and…

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